Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Abstract Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate applications of …

Abstract We present the Pathways [1] Autoregressive Text-to-Image (Parti) model, which
generates high-fidelity photorealistic images and supports content-rich synthesis involving …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of
Transformer-based neural language models specialized for dialog, which have up to 137B …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

Recently Transformer and Convolution neural network (CNN) based models have shown
promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural …

Neural network scaling has been critical for improving the model quality in many real-world
machine learning applications with vast amounts of training data and compute. Although this …

Scaling up training datasets and model parameters have benefited neural network-based
language models, but also present challenges like distributed compute, input data …